Look up any arxiv paper on alphaxiv.org to get a structured AI-generated overview. This is faster and more reliable than trying to read a raw PDF.
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Post material interim assignment updates, then commit and submit a terminal experiment result through the typed Senpai GitHub tools.
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安装前可以查看交给 AI 代理的指令内容。
Use post_assignment_comment when the advisor asked for a response or the
experiment is blocked or materially delayed. Use a fresh comment_id for each
new message and reuse that ID only for an exact retry. Keep the PR concise
rather than mirroring routine internal narration.
First make the repository worktree clean by committing only the assigned change.
Collect the current local commit SHA and the current remote assignment-branch
SHA. Build the strict ExperimentResult required by
submit_experiment_result:
Call submit_experiment_result with exactly this operation-specific payload. Do not add PR-body
text, per-run metrics, hyperparameters, or aliases such as head_sha,
previous_head_sha, success, or min; put those details in the bounded
summary instead.
{
"branch": "student/experiment",
"remote_branch_sha_before_push": "REMOTE_SHA_BEFORE_PUSH",
"result": {
"assignment": {
"repo": "owner/repo",
"pr_number": 123,
"assignment_id": "assignment-id",
"revision_id": "LATEST_REVISION_ID",
"expected_head_sha": "LOCAL_COMMIT_SHA",
"student": "student-name"
},
"status": "succeeded",
"hypothesis": "The falsifiable hypothesis tested.",
"summary": "The conclusion, evidence, caveats, and important per-run metrics (maximum 4,000 characters).",
"runs": [
{
"run_id": "wandb-run-id",
"url": "https://wandb.ai/entity/project/runs/wandb-run-id",
"state": "finished"
}
],
"primary_metric": {
"name": "validation/metric",
"direction": "minimize",
"baseline": 1.23,
"candidate": 1.10,
"delta": -0.13
},
"commit_sha": "LOCAL_COMMIT_SHA"
}
}
Use one of succeeded, failed, inconclusive, or cancelled for result
status; minimize or maximize for metric direction; and finished,
failed, crashed, or killed for each run. primary_metric may be omitted
when no finite comparison exists. Refresh the PR first and use its latest
assignment revision_id; a mid-turn advisor revision supersedes an earlier
one.
That single tool call derives the PR and local result head from result,
lease-pushes the clean assignment branch, verifies the new PR head, upserts the
authenticated structured result, marks the PR ready,
and reconciles status:review. That label is the durable advisor notification.
Exact replay of the same result is safe. Once result evidence is published for
that revision and commit, changed evidence requires a new commit or assignment
revision; the tool will not rewrite the reviewed result in place.
Do not run git push, edit labels, write result markers, or call gh pr ready
yourself.
If any run is still active or could change the conclusion, keep the assignment
in progress and register it with monitor_training; do not submit a terminal
result.
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Look up any arxiv paper on alphaxiv.org to get a structured AI-generated overview. This is faster and more reliable than trying to read a raw PDF.
中文简介正在准备中。当前显示原文说明。
Operator-side analysis of historical ML experiment PRs in Senpai research tracks. Use this skill whenever the user asks to: analyze experiments, categorize PRs, bucket experiments, summarize what's been tried, understand experiment history, review merged vs closed results, or asks "what experiments have we run / worked / failed". Also triggers for: "pull the latest experiments", "what's been tried so far", "category breakdown of PRs", "which experiments succeeded", "noam track analysis". When a branch name is mentioned (e.g. "noam branch", "on the noam branch"), pass it as the base branch to scope the fetch to just those PRs.
中文简介正在准备中。当前显示原文说明。
Create a typed assignment branch and draft PR for one student. Use when the advisor has a concrete hypothesis and the student has no open `status:wip` or `status:review` assignment.
中文简介正在准备中。当前显示原文说明。
Create or improve a Senpai target repository's program.md. Use this skill whenever the user wants to point Senpai at a fresh ML or research target repository, define the research objective, primary metric, benchmark contract, allowed edit boundaries, W&B reporting contract, or prepare a repo for autonomous advisor/student experiment loops.
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Open GitHub Issues for human input and respond to the researcher team. Use this skill whenever you need to handle a human_issue event, respond to human issues, ask humans a question, or check team communications. Also triggers for: "any human messages?", "check issues", "respond to humans".
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Choose, configure, launch, and collect bounded subagents for research and engineering decisions. Root agents and delegation-capable subagents should read this before delegating: every task requires an explicit model tier, and high-leverage work such as research ideation, round planning, plateau pivots, large research reviews, hard optimization, disputed evidence, and expensive experiment portfolios requires frontier judgment.
中文简介正在准备中。当前显示原文说明。